Advertisement

Context Information for Understanding Forest Fire Using Evolutionary Computation

  • L. Usero
  • A. Arroyo
  • J. Calvo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4528)

Abstract

One of the major forces for understanding forest fire risk and behavior is the fire fuel. Fire risk and behavior depend on the fuel properties such as moisture content. Context information on vegetation water content is vital for understanding the processes involved in initiation and propagation of forest fires. In that sense, a novel method was tested to estimate vegetation canopy water content (CWC) from simulated MODIS satellite data. An inversion of a radiative transfer model called Forest Light Interaction-Model (FLIM) from performed using evolutionary computation. CWC is critical, among other applications, in wildfire risk assessment since a decrease in CWC causes higher probability to have wildfire occurrence. Simulations were carried out with the FLIM model for a wide range of forest canopy characteristics and CWC values. A 50 subsample of the simulations was used for the training process and 50 for the validation providing a RMSE=0.74 and r2=0.62. Further research is needed to apply this method on real MODIS images.

Keywords

Genetic Programing Vegetation Water Content Forest Fire Understanding 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gao, B.-C., Goetz, A.F.H.: Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data. Remote Sensing of Environment 52(3), 155–162 (1995)CrossRefGoogle Scholar
  2. 2.
    Chuvieco, E., Cocero, D., Riaño, D., Martin, P., Martínez-Vega, J., de la Riva, J., et al.: Combining NDVI and Surface Temperature for the estimation of live fuels moisture content in forest fire danger rating. Remote Sensing of Environment 92(3), 322–331 (2004)CrossRefGoogle Scholar
  3. 3.
    Goel, N.S.: Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data. Remote Sensing Reviews 4, 1–212 (1988)MathSciNetGoogle Scholar
  4. 4.
    Rosema, A., Verhoef, W., Noorbergen, H., Borgesius, J.J.: A new forest light interaction model in support of forest monitoring. Remote Sensing of Environment 42, 23–41 (1995)CrossRefGoogle Scholar
  5. 5.
    Xiao, X., Boles, S., Liu, J., Zhuang, D., Frolking, S., Li, C., et al.: Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sensing of Environment 95(4), 480–492 (2005)CrossRefGoogle Scholar
  6. 6.
    Ceccato, P., Gobron, N., Flasse, S., Pinty, B., Tarantola, S.: Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 - Theoretical approach. Remote Sensing of Environment 82(2-3), 188–197 (2002)CrossRefGoogle Scholar
  7. 7.
    Riaño, D., Vaughan, P., Chuvieco, E., Zarco-Tejada, P.J., Ustin, S.L.: Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content (In press)Google Scholar
  8. 8.
    Riaño, D., Ustin, S.L., Usero, L., Patricio, M.Á.: Estimation of Fuel Moisture Content Using Neural Networks. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 489–498. Springer, Heidelberg (2005)Google Scholar
  9. 9.
    Fang, H., Liang, S., Kuusk, A.: Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model. Remote Sensing of Environment 85, 257–270 (2003)CrossRefGoogle Scholar
  10. 10.
    Evolutionary Computation Laboratory (ECLab). Evolutive Computation based in Java (EJC). George Mason University, http://cs.gmu.edu/~eclab/projects/ecj/

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • L. Usero
    • 3
    • 4
  • A. Arroyo
    • 2
  • J. Calvo
    • 1
  1. 1.Dpto. de Organización y Estructura de la información, Universidad Politécnica de MadridSpain
  2. 2.Dpto. de Sistemas Inteligentes Aplicados, Universidad Politécnica de MadridSpain
  3. 3.Dpto. Ciencias de la Computación, Universidad de AlcaláSpain
  4. 4.Center for Spatial Technologies and Remote Sensing, U. California. One Shields Ave. 95616-8617 Davis, CA.USA

Personalised recommendations